APLICACIÓN DEL ESPECTROGRAMA MODIFICADO PARA LA IDENTIFICACIÓN DE MÚLTIPLES FALLOS COMBINADOS EN MOTORES DE INDUCCIÓN ALIMENTADOS POR INVERSORES (THE APPLICATION OF MODIFIED SPECTROGRAM FOR IDENTIFYING MULTIPLE COMBINED FAULTS IN INVERTER-FED INDUCTION MOTORS)
Resumen
Actualmente, las industrias utilizan motores de inducción alimentados con variadores de velocidad, los cuales generan componentes armónicos en la corriente del estator. Por lo tanto, es importante la detección y el diagnóstico temprano de fallas en el motor de inducción para su uso en el mantenimiento basado en condiciones. Sin embargo, la mayoría de los métodos se ocupan de un único fallo. La contribución de esta investigación es la aplicación de una estrategia de monitoreo de condición que puede realizar evaluaciones precisas y confiables de la presencia de condiciones de falla única o combinada en motores de inducción. El artículo presenta una descripción del estado del arte en el monitoreo de fallas y establece los métodos usados para la identificación de estas fallas, usando el método del espectrograma reasignado. Se analizan tres tipos de fallas y en los resultados pueden verse la adecuada identificación de estas usando espectros de tiempo-frecuencia. Los resultados muestran que el método del espectrograma reasignado podría utilizarse como técnica de detección determinista; donde las frecuencias de los fallos son muy cercanas a las reportadas analíticamente en la literatura.
Palabras Clave: Monitoreo de la condición, diagnóstico de fallas, motores de inducción, espectrograma reasignado, análisis espectral.
Abstract
Currently, industries use induction motors fed with variable speed drives, which generate harmonic components in the stator current. Therefore, it is important early failure detection and diagnosis in induction motor for use in condition-based maintenance. However, most of the methods deal with a single fault, only. In electrical equipment with multiple faulty conditions present; it is critical to differentiate between the single or combined faulty conditions; so, it is important to differentiate between these. The contribution of this research is the application of a condition monitoring strategy that can make accurate and reliable assessments of the presence of single or combined fault conditions in induction motors. The article presents a description of the state of the art in fault monitoring and establishes the methods used for the identification of these faults, using the reassigned spectrogram method. Three types of faults are analyzed, and the results show the proper identification of these faults using time-frequency spectra. Results show the reassigned spectrogram method could be used as a deterministic detection technique; where the fault frequencies are very close to those analytically reported in literature.
Keywords: Condition monitoring, Fault diagnosis, Induction motors, Reassigned Spectrogram, Spectral analysis.
Texto completo:
467-485 PDFReferencias
Amezquita-Sanchez, J.P., Valtierra-Rodriguez, M., Camarena-Martinez D., Granados-Lieberman, D., Romero-Troncoso, R.J., & Dominguez-Gonzalez, A. (2016). Fractal dimension-based approach for detection of multiple combined faults on induction motors. SAGE Journal of Vibration and Control, 22(13), 3638-3648.
Antonino-Daviu, J., Jover Rodriguez, P., Riera-Guasp, M., Pineda-Sánchez M., & Arkkio, A. (2009). Detection of combined faults in induction machines with stator parallel branches through the DWT of the startup current. ELSEVIER Mechanical Systems and Signal Processing, 23, 2336–2351.
Antonino-Daviu, J., Quijano-López, A., Climente-Alarcon, V., & Garín-Abellán, C. (2018). Reliable Detection of Rotor Winding Asymmetries in Wound Rotor Induction Motors via Integral Current Analysis. IEEE Transactions on Industry Applications, 53(3), 2040-2048.
Bengherbia, B., Zmirli, M.O., Toubal, A., Guessoum, A. (2020). FPGA-based wireless sensor nodes for vibration monitoring system and fault diagnosis. ELSEVIER Measurement, 101, 81-92.
Carbajal-Hernández, J.J., Sánchez-Fernández, L.P., Hernández-Bautista, I., Medel-Juárez, J.J., & Sánchez-Pérez, L.A. (2017). Classification of unbalance and misalignment in induction motors using orbital analysis and associative memories. ELSEVIER Neurocomputing, 175, 838-850.
Fernandez-Cavero, V., Morinigo-Sotelo, D., Duque-Perez, O., & Pons-Llinares, J. (2018). Fault Detection in Inverter-fed Induction Motors in Transient Regime: State of the Art. IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 205-211.
Fulop, S. A. (2013). Speech Spectrum Analysis, 1st ed., Springer, 127-137.
Gangsar, P., & Tiwari, R. (2019). Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms. ELSEVIER Mechanical Systems and Signal Processing, 94, 464-48.
Garcia-Perez, A., Romero-Troncoso, R. J., Cabal-Yepez, E., & Osornio-Rios, R.A. (2014). The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors. IEEE Trans. Ind. Electron., 58(5), 2002-2010.
Gritli, Y., Rossi, C., Casadei, D., Filippetti, F., & Capolino, G.-Andre, (2017). A Diagnostic Space Vector-Based Index for Rotor Electrical Fault Detection in Wound-Rotor Induction Machines Under Speed Transient. IEEE Trans. Ind. Electron., 64(5), 3892-3902.
Karvelis, P., Georgoulas, G., Tsoumas, I.P., Antonino-Daviu, J.A., Climente-Alarcón, V., & tylios, C.D. (2019). A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors. IEEE Transactions on Industrial Informatics 11(5), 1028-1037.
Kia, S.H., Cirrincione, G., Henao, H., & Capolino,. G.-A. (2018) A computationally efficient algorithm devoted to gear tooth localized fault detection in induction machine-based systems. XXII International Conference on Electrical Machines (ICEM), 2144-2150.
Liu, Y., & Bazzi, A.M. (2018). A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art. Elsevier ISA Transactions, 70, 400–409.
Madescu, G., Biriescu, M., Tutelea, L.N., Mot, M., Svoboda, M., & Boldea, I. (2020). Experimental Investigation of Rotor Currents Distribution in the Healthy and Faulty Cage of Induction Motors at Standstill. IEEE Trans. Ind. Electron., 64(7), 5305-5313.
Nelson, D. J. (2012). Instantaneous higher order phase derivatives. Digital Signal Processing, 12, 416-428.
Panigrahy, P.S., Konar, P., & Chattopadhyay, P. (2019). Application of Data Mining in Fault Diagnosis of Induction Motor. IEEE First International Conference on Control, Measurement and Instrumentation (CMI), 274-278.
Pantea, A., Yazidi, A., Betin, F., Taherzadeh, M., Carrière, S., Henao, H., & Capolino, G.-A. (2018). Six-Phase Induction Machine Model for Electrical Fault Simulation Using the Circuit-Oriented Method. IEEE Trans. Ind. Electron., 63(1), 494-503.
Rajan Babu, W., & Ravichandran C. S. (2017). Comprehensive Review on Fault Detection in Induction Motor. International Journal of Applied Engineering Research, 10, 43630-43634.
Rangel-Magdaleno, J., Peregrina-Barreto, H., Ramirez-Cortes, J., Morales-Caporal, R., & Cruz-Vega, I. (2019). Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT. Hindawi Shock and Vibration, 2019, 1-10.
Romero-Troncoso, R.J., Garcia-Perez, A., Morinigo-Sotelo, D., Duque-Perez, O., Osornio-Rios, R.A., & Ibarra-Manzano, M.A., (2016). Rotor unbalance and broken rotor bar detection in inverter-fed induction motors at start-up and steady-state regimes by high-resolution spectral analysis. ELSEVIER Electric Power System Research, 133, 142–148.
Smart, E., Brown D., & Axel-Berg, L. (2015). Comparing one and two class classification methods for multiple fault detection on an induction motor. IEEE Symposium on Industrial Electronics and Applications (ISIEA), 244-251.
Sudhakar, I., Adi Narayana, S., & Anil Prakash, M. (2020). Condition Monitoring of a 3-Ø Induction Motor by Vibration Spectrum analysis using Fft Analyser- A Case Study. ELSEVIER Materials Today: Proceedings, 4, 1099-1105.
Trachi, Y., Elbouchikhi, E., Choqueuse, V., & Benbouzid, M.E.H. (2018). Induction Machines Fault Detection Based on Subspace Spectral Estimation. IEEE Trans. Ind. Electron., 63(9), 5641-5651.
Vilhekar, T.G., Ballal, M.S., & Suryawanshi, H.M. (2017). Application of Multiple Parks Vector Approach for Detection of Multiple Faults in Induction Motors. Journal of Power Electronics, 1,1-12.
URL de la licencia: https://creativecommons.org/licenses/by/3.0/deed.es
Pistas Educativas está bajo la Licencia Creative Commons Atribución 3.0 No portada.
TECNOLÓGICO NACIONAL DE MÉXICO / INSTITUTO TECNOLÓGICO DE CELAYA
Antonio García Cubas Pte #600 esq. Av. Tecnológico, Celaya, Gto. México
Tel. 461 61 17575 Ext 5450 y 5146
pistaseducativas@itcelaya.edu.mx